Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Building Better Test Functions
Proceedings of the 6th International Conference on Genetic Algorithms
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Inheriting Parents Operators: A New Dynamic Strategy for Improving Evolutionary Algorithms
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
The parameter-less genetic algorithm in practice
Information Sciences—Informatics and Computer Science: An International Journal
Human evolutionary model: A new approach to optimization
Information Sciences: an International Journal
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
A self-adaptive migration model genetic algorithm for data mining applications
Information Sciences: an International Journal
Adapting operator settings in genetic algorithms
Evolutionary Computation
A genetic algorithm calibration method based on convergence due to genetic drift
Information Sciences: an International Journal
A Compass to Guide Genetic Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Automatic parameter tuning with a Bayesian case-based reasoning system. A case of study
Expert Systems with Applications: An International Journal
Adaptive parameter control of evolutionary algorithms to improve quality-time trade-off
Applied Soft Computing
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
Improving genetic algorithms performance via deterministic population shrinkage
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Automatic algorithm configuration based on local search
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Relevance estimation and value calibration of evolutionary algorithm parameters
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Improving the success of recombination by varying broodsize and sibling rivalry
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Ensemble strategies with adaptive evolutionary programming
Information Sciences: an International Journal
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
A note on the learning automata based algorithms for adaptive parameter selection in PSO
Applied Soft Computing
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Statistical exploratory analysis of genetic algorithms
IEEE Transactions on Evolutionary Computation
Are state-of-the-art fine-tuning algorithms able to detect a dummy parameter?
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
On the performance comparison of multi-objective evolutionary UAV path planners
Information Sciences: an International Journal
A novel selection evolutionary strategy for constrained optimization
Information Sciences: an International Journal
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
Block-matching algorithm based on harmony search optimization for motion estimation
Applied Intelligence
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The issue of controlling values of various parameters of an evolutionary algorithm is one of the most important and interesting areas of research in evolutionary computation. In this paper we propose two new parameter control strategies for evolutionary algorithms based on the ideas of reinforcement learning. These strategies provide efficient and low-cost adaptive techniques for parameter control and they preserve the original design of the evolutionary algorithm, as they can be included without changing either the structure of the algorithm nor its operators design.